• Autonomous Mapping and Navigation Using SLAM Toolbox, Nav2, Gazebo, and Rviz Visualization
• GLC: Semantic Graph-Guided Coarse-Fine-Refine Full Loop Closing for LiDAR SLAM
• Drift-free Visual SLAM using Digital Twins
• pySLAM: a visual SLAM pipeline in Python for monocular, stereo and RGBD cameras.
• pySLAM SLAM pipeline updates
• ICRA 2025 Gaussian-LIC: Real-Time Photo-Realistic SLAM with Gaussian Splatting and LiDAR-Inertial-Camera Fusion
• An unofficial open source implentation of CSIRO's Wildcat SLAM.
• DynoSAM: Dynamic Object Smoothing and Mapping for Dynamic SLAM
• [Present and Future of SLAM in Extreme Environments](https://ieeexplore.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

See how a minor change to your commit message style can make a difference.

git commit -m"<type>(<optional scope>): <description>" \
  -m"<optional body>" \
  -m"<optional footer>"

This guide documents the process of installing LineageOS with microG and passing PlayIntegrity without logging into any Google accounts. It also ensures SMS and GPS functionality while maintaining privacy.

• A device compatible with LineageOS.
• Familiarity with unlocking bootloaders, flashing custom ROMs, and using terminal commands.